Fire Monitoring with a Fixed-wing Unmanned Aerial Vehicle
Bibliographic record
Abstract
When it comes to wildfire surveillance missions, Unmanned Aerial Vehicles (UAVs) offer a safer alternative over manned aircraft in such dangerous flight conditions. Furthermore, the efficiency of fixed-wing UAVs, as compared to multi-rotors platforms, makes them more desirable for prolonged missions with sustained surveillance. Therefore, while previous research has explored autonomous monitoring of fires with multi-rotor UAVs, this work focuses on developing an approach for fire monitoring with a fixed-wing UAV. In order to autonomously track the fire as it propagates, images of the fire from an on-board IR camera are first processed to extract an edge of the fire front. The proposed algorithm then guides the UAV to fly towards the fire front and track it, by obtaining a reference point located on the extracted fire edge, and using L1guidance law to command the aircraft. Furthermore, as the UAV navigates around the fire, a map of the fire is constructed on-board the vehicle, using a fire occupancy grid map to denote the probability of a fire in each cell. Results from two simulations, with fire data obtained from WRF-Fire simulations, demonstrate the ability for the UAV to autonomously track the propagating fire, regardless of its shape or scale, and maintain a map of the fire on-board the vehicle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".